The energy demand in current times has increased greatly in last few years. This increasing demand calls for a sustainable and clean energy resource that would reduce the load on non-renewable resources. Wind energy is a renewable resource which is harnessed by mankind from an ancient era. So as to meet this increasing energy demands, innovation in the field of wind energy is required. A wind turbine generates electricity but achieving optimal power is a difficult task. In this case, the optimal power is defined as maximum power produced but at the constraints that the fatigue loading of the wind turbine structure should be as minimum as possible. Also, the wind turbine parameters such as rotor speed and pitch activity should be in a safe operational region. The problem in controlling wind turbines is that they work in a highly uncertain environment where managing so many factors at the same time are difficult. Optimal control of wind turbine has helped in achieving maximum power with-in safe working limits. Due to high uncertainty in the operating conditions of a wind turbine, it is quite a daunting task to find optimal gains for a wind turbine controller. This thesis focuses on achieving optimal gain parameters for wind turbine controller by using an algorithm from machine learning community. In this thesis the problem is formulated as a supervised learning problem where input-output mapping has to be predicted. For this purpose, GPRT is used. The reason behind using GPRT is it takes fewer number of measurements to give good prediction compared to others. The property of GPRT where it deals with uncertain and non linear data with ease, making it a good choice for predicting wind turbine controller gains. The second part of this thesis contains optimisation of the surrogate model achieved by performing regression. The optimisation is done by Monte Carlo Maximum distribution and improved results were generated by applying sequential sampling to this algorithm. This helps us to get a likelihood of optimal gains where the wind turbine gives out rated power with minimal fatigue loads, pitch activity and least deviation of rotor speed from rated. The results obtained from the likelihood was tested for different operational wind speed and also tested for Extreme Operating Gusts as part of disturbance rejection and compared to current parameter used. The comparison shows considerable improvement in the fatigue loads and pitch activity with having improvement in power production. In second case study, more parameters were predicted and optimised using the same algorithm so that the potential of this algorithm can be estimated. This was also performed successfully which proves that this technique can successfully be used to solve higher dimensional problems of wind turbine control.

Systems and Control

Advisors/Committee Members: van Wingerden, Jan-Willem (mentor), Engels, Wouter (mentor), Kober, Jens (graduation committee), Mulders, Sebastiaan (graduation committee), Delft University of Technology (degree granting institution).

Surrogate modeling is a family of engineering techniques that attracts great interest today and can be applied in many challenging fields. A big advantage of it is that surrogate models (models based on these techniques) offer reliable results by being computationally cheaper than other candidate models. The savings in computational time is usually paramount for problems that involve a lot of variables and parameters and many iterative processes. In the wind energy industry in particular, the design of the best layout of the wind farm is a problem that has been presented in the literature as an optimization problem; that is, a problem to optimize the wind farm layout in respect to some objective the modeler deems appropriate. More often than not, maximizing the expected power of the layout is mainly considered as this objective. The layout's expected power is  – among other things  – heavily dependent on the layout and the wake interactions between the turbines. The iterative search among many layouts to find the best one can be done with the help of a well-known optimization tool, the binary genetic algorithm. However, this tool cannot work alone, it solely facilitates the search over an adequate number of candidate solutions. To make it work, the modeler should provide it with some model that assesses how good in terms of the objective that has been set. In this thesis therefore, the theory, the development and the use of two models of interest are investigated: Gaussian Process Regression (a surrogate model) and the Monte Carlo Method (a method based on random sampling). Great care was given to compile the theoretical basis of these models in order to be a good reference point for the non-experienced reader. The nature of these two models differs quite a bit, but they both can be used by the modeler to yield interesting results. These results will be compared to each other and against a third model's results, a specific wake model. This third model is the Original Model which the Gaussian Process Regression model and the Monte Carlo Method model utilize and compare against. The reliability of the results and computational speed will be the measure of success and ranking for these three models. Finally, the comparison of the three models continues in how potent they are to propose an optimized layout for a wind farm. Each of the three models is coupled with the binary genetic algorithm that is developed specifically to connect with them. Afterwards, the proposed best layouts are discussed. The results show that the Gaussian Process Regression model performs reliably and very fast in comparison to the Original model. On the other hand, the Monte Carlo model, although also fast when it is used to find an optimized layout, could not be verified that it performs reliably and therefore, its results cannot be trusted without going into further investigation. After the comparison, further discussion follows with some recommendations for future research.

Aerospace Engineering | Aerodynamics…

Advisors/Committee Members: Quaeghebeur, Erik (mentor), van Bussel, Gerard (graduation committee), Dwight, Richard (graduation committee), Delft University of Technology (degree granting institution).

This master thesis aims to gain new empirical insights into longitudinal driving behavior by means of the enumeration of a new hybrid car-following (CF) model which combines parametric and non parametric formulation. On one hand, the model, which predicts the drivers acceleration given a set of variables, benefits from innovative machine learning techniques such as Gaussian process regression (GPR) to make predictions when there exist correlation between new input and the training dataset. On the other hand, it uses existent traditional parametric CF models to predict acceleration when no similar situations are found in the training dataset. This formulation guarantees a complete and continues model and deals with the challenges of new available types of dataset in the transport field: noisy and incomplete yet with large amount of data. Multiple models have been trained using the Optimal Velocity Model (OVM) as a basis parametric model and a dataset collected in the PPA project in Amsterdam by traffic radar detection in stop and go traffic conditions. The other main innovation of this thesis is that variables rarely included in any CF model such as the status and the distance of drivers to the traffic light are also analyzed. Results show that the GPR model formulation is robust as the model performs better than OVM alone according to the main KPI, but still collisions occasionally occur. Moreover, results depict that traffic light status actively influences driver behavior. Overall, this thesis gives insights into new powerful mathematical techniques that can be applied to describe longitudinal driving behavior or any modeled process. Advisors/Committee Members: Hoogendoorn, Serge (mentor), Knoop, Victor (graduation committee), Steenbakkers, Jeroen (graduation committee), Alonso Mora, Javier (graduation committee), Delft University of Technology (degree granting institution).